OECD Data Live dataset
Data - OECD
Info
Data on macro
source | dataset | .html | .RData |
---|---|---|---|
2024-09-14 | 2024-09-14 | ||
2024-09-14 | 2024-09-14 | ||
2024-09-14 | 2024-09-14 | ||
2024-09-14 | 2024-09-14 | ||
2024-09-14 | 2024-09-14 | ||
2024-09-14 | 2024-09-14 | ||
2024-09-04 | 2024-09-14 | ||
2024-09-14 | 2024-09-14 | ||
2024-08-21 | 2024-09-14 | ||
2024-09-14 | 2024-09-14 | ||
2024-09-02 | 2024-09-02 | ||
2024-08-29 | 2024-09-14 | ||
2024-06-06 | 2024-06-30 | ||
2024-09-11 | 2024-06-30 | ||
2024-09-11 | 2024-06-30 | ||
2024-07-01 | 2024-04-11 | ||
2024-07-01 | 2024-06-30 | ||
2024-09-14 | 2024-09-15 | ||
2024-09-14 | 2024-09-14 | ||
2024-09-14 | 2024-09-14 | ||
2024-09-14 | 2024-09-14 | ||
2024-09-14 | 2024-09-14 | ||
2024-09-14 | 2024-09-14 | ||
2024-09-14 | 2024-09-14 |
LAST_COMPILE
LAST_COMPILE |
---|
2024-09-15 |
Last
obsTime | Nobs |
---|---|
2030 | 507 |
INDICATOR
Code
%>%
DP_LIVE left_join(DP_LIVE_var$INDICATOR, by = "INDICATOR") %>%
group_by(INDICATOR, Indicator) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
SUBJECT
Code
%>%
DP_LIVE left_join(DP_LIVE_var$SUBJECT, by = "SUBJECT") %>%
group_by(SUBJECT, Subject) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
INDICATOR, SUBJECT
Code
%>%
DP_LIVE left_join(DP_LIVE_var$INDICATOR, by = "INDICATOR") %>%
left_join(DP_LIVE_var$SUBJECT, by = "SUBJECT") %>%
group_by(INDICATOR, Indicator, SUBJECT, Subject) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
MEASURE
Code
%>%
DP_LIVE left_join(DP_LIVE_var$MEASURE, by = "MEASURE") %>%
group_by(MEASURE, Measure) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
LOCATION
Code
%>%
DP_LIVE left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION, Location) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
FREQUENCY
Code
%>%
DP_LIVE left_join(DP_LIVE_var$FREQUENCY, by = "FREQUENCY") %>%
group_by(FREQUENCY, Frequency) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
FREQUENCY | Frequency | Nobs |
---|---|---|
A | Annual | 1109946 |
M | Monthly | 759555 |
Q | Quarterly | 515873 |
INDICATOR, SUBJECT, FREQUENCY
Code
%>%
DP_LIVE left_join(DP_LIVE_var$INDICATOR, by = "INDICATOR") %>%
left_join(DP_LIVE_var$SUBJECT, by = "SUBJECT") %>%
group_by(INDICATOR, Indicator, SUBJECT, Subject, FREQUENCY) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
obsTime
Code
%>%
DP_LIVE group_by(obsTime) %>%
summarise(Nobs = n()) %>%
arrange(desc(obsTime)) %>%
print_table_conditional()
Saving Rate - SAVING
Saving is equal to the difference between disposable income (including an adjustment for the change in employment-related pension entitlements) and final consumption expenditure. It reflects the part of disposable income that, together with the incurrence of liabilities, is available to acquire financial and non-financial assets. The saving rate presented here corresponds to net saving, which is saving net of depreciation, as percentage of gross domestic product (GDP). All OECD countries compile their data according to the 2008 System of National Accounts (SNA).
United States, France
Code
%>%
DP_LIVE filter(INDICATOR == "SAVING",
%in% c("FRA", "USA"),
LOCATION == "TOT",
SUBJECT == "PC_GDP") %>%
MEASURE left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
year_to_date() %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue/100) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
theme_minimal() + xlab("") + ylab("Saving Rate (% of GDP)") +
+ scale_color_identity() +
add_2flags scale_y_continuous(breaks = 0.01*seq(-100, 260, 2),
labels = percent_format(accuracy = 1, p = "")) +
scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
labels = date_format("%Y"))
United States, France, Germany, Italy
Code
%>%
DP_LIVE filter(INDICATOR == "SAVING",
%in% c("FRA", "USA", "DEU", "ITA"),
LOCATION == "TOT",
SUBJECT == "PC_GDP") %>%
MEASURE left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
year_to_date() %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue/100) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
theme_minimal() + xlab("") + ylab("Saving Rate (% of GDP)") +
+ scale_color_identity() +
add_4flags scale_y_continuous(breaks = 0.01*seq(-100, 260, 2),
labels = percent_format(accuracy = 1, p = "")) +
scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
labels = date_format("%Y"))
Money
Narrow Money - M1
Table
Code
%>%
DP_LIVE filter(INDICATOR == "M1",
== "M") %>%
FREQUENCY left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
select_if(~ n_distinct(.) > 1) %>%
group_by(LOCATION, Location) %>%
summarise(Nobs = n(),
obsValue = last(obsValue)) %>%
arrange(-obsValue) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
Japan, United Kingdom, USA
Code
%>%
DP_LIVE filter(LOCATION %in% c("JPN", "GBR", "USA"),
== "M1",
INDICATOR == "M") %>%
FREQUENCY %>%
month_to_date left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
theme_minimal() + xlab("") + ylab("Narrow Money - M1") +
scale_x_date(breaks = seq(1940, 2030, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = c(1, 2, 3, 5, 10, 20, 50, 80, 100, 200, 300, 500, 800))
Euro area, U.S.
Code
%>%
DP_LIVE filter(LOCATION %in% c("EA19", "USA", "JPN"),
== "M1",
INDICATOR == "M") %>%
FREQUENCY %>%
month_to_date left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
theme_minimal() + xlab("") + ylab("Narrow Money - M1") +
scale_x_date(breaks = seq(1940, 2030, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = c(1, 2, 3, 5, 10, 20, 50, 80, 100, 200, 300, 500, 800))
Broad Money - M3
Table
Code
%>%
DP_LIVE filter(INDICATOR == "M3",
== "M") %>%
FREQUENCY left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
select_if(~ n_distinct(.) > 1) %>%
group_by(LOCATION, Location) %>%
summarise(Nobs = n(),
obsValue = last(obsValue)) %>%
arrange(-obsValue) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
Japan, United Kingdom, USA
Code
%>%
DP_LIVE filter(LOCATION %in% c("JPN", "GBR", "USA"),
== "M3",
INDICATOR == "M") %>%
FREQUENCY %>%
month_to_date left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
theme_minimal() + xlab("") + ylab("Broad Money - M3") +
scale_x_date(breaks = seq(1940, 2030, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = c(1, 2, 3, 5, 10, 20, 50, 80, 100, 200, 300, 500, 800))
Euro area, U.S.
Code
%>%
DP_LIVE filter(LOCATION %in% c("EA19", "USA", "JPN"),
== "M3",
INDICATOR == "M") %>%
FREQUENCY %>%
month_to_date left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
theme_minimal() + xlab("") + ylab("Broad Money - M3") +
scale_x_date(breaks = seq(1940, 2030, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = c(1, 2, 3, 5, 10, 20, 50, 80, 100, 200, 300, 500, 800))
Gross domestic spending on R&D - GDEXPRD
Table
Code
%>%
DP_LIVE filter(INDICATOR == "GDEXPRD",
== "TOT",
SUBJECT == "PC_GDP") %>%
MEASURE left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION, Location) %>%
arrange(obsTime) %>%
summarise(date_min = min(obsTime),
date_max = max(obsTime),
obsValue = last(obsValue)) %>%
arrange(-obsValue) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
Korea, Israel, Sweden, Japan, Taiwan
Code
%>%
DP_LIVE filter(INDICATOR == "GDEXPRD",
%in% c("KOR", "ISR", "SWE", "JPN", "TWN"),
LOCATION == "TOT",
SUBJECT == "PC_GDP") %>%
MEASURE left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
year_to_date() %>%
mutate(Location = ifelse(LOCATION == "TWN", "Taiwan", Location)) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(Location = ifelse(LOCATION == "TWN", "Taiwan", Location)) %>%
mutate(obsValue = obsValue/100) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
theme_minimal() + xlab("") + ylab("Gross domestic spending on R&D") +
+ scale_color_identity() +
add_5flags scale_y_continuous(breaks = 0.01*seq(0, 260, .2),
labels = percent_format(accuracy = .1, p = "")) +
scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
labels = date_format("%Y"))
United States, France, Germany, Italy, Japan
Code
%>%
DP_LIVE filter(INDICATOR == "GDEXPRD",
%in% c("USA", "FRA", "DEU", "ITA", "JPN"),
LOCATION == "TOT",
SUBJECT == "PC_GDP") %>%
MEASURE left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
year_to_date() %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue/100) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
theme_minimal() + xlab("") + ylab("Gross domestic spending on R&D") +
+ scale_color_identity() +
add_5flags scale_y_continuous(breaks = 0.01*seq(0, 260, .2),
labels = percent_format(accuracy = .1, p = "")) +
scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
labels = date_format("%Y"))
Spain, Portugal
Code
%>%
DP_LIVE filter(INDICATOR == "GDEXPRD",
%in% c("ESP", "PRT"),
LOCATION == "TOT",
SUBJECT == "PC_GDP") %>%
MEASURE left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
year_to_date() %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue/100) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
theme_minimal() + xlab("") + ylab("Gross domestic spending on R&D") +
+ scale_color_identity() +
add_2flags scale_y_continuous(breaks = 0.01*seq(0, 260, .2),
labels = percent_format(accuracy = .1, p = "")) +
scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
labels = date_format("%Y"))
OILPROD - Crude oil production
Info
Crude oil production in DP_LIVE is expressed as KTOE - thousands of ton of oil equivalent. (toe)
95 Millions of barrels / day = 34.7 Bn of barrels / year
1 barrel of oil equivalent (boe) = 0.14 ton of oil equivalent (toe).
1 ton of oil equivalent = 1/0.14 = 7.14 barrels of oil equivalent.
Table
Code
%>%
DP_LIVE filter(INDICATOR == "OILPROD") %>%
left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
select_if(~ n_distinct(.) > 1) %>%
filter(!is.na(obsValue)) %>%
group_by(LOCATION, Location) %>%
arrange(obsTime) %>%
summarise(date_min = min(obsTime),
date_max = max(obsTime),
obsValue = last(obsValue)) %>%
mutate(obsValue = obsValue/10^6/0.14) %>%
arrange(-obsValue) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
Table - World
Code
<- DP_LIVE %>%
OILPRODWLD filter(INDICATOR == "OILPROD",
== "WLD") %>%
LOCATION select_if(~n_distinct(.) > 1) %>%
filter(!is.na(obsValue)) %>%
%>%
year_to_date transmute(date, variable = "OILPRODWLD", value = obsValue)
save(OILPRODWLD, file = "DP_LIVE_OILPRODWLD.RData")
World, Saudi Arabia, Russia
Code
%>%
DP_LIVE filter(INDICATOR == "OILPROD",
%in% c("WLD", "RUS", "SAU")) %>%
LOCATION left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
left_join(DP_LIVE_var$INDICATOR, by = "INDICATOR") %>%
filter(!is.na(obsValue)) %>%
year_to_date() %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue/10^6/0.14) %>%
# 0.14 =
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
theme_minimal() + xlab("") + ylab("Billions of Barrels of Oil") +
+ scale_color_identity() +
add_3flags scale_y_log10(breaks = c(1, 2, 3, 4, 5, 8, 10, 15, 20, 30),
labels = dollar_format(a = 1, pre = "", su = " Bn")) +
scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
labels = date_format("%Y"))
USA, Iraq, Iran
Code
%>%
DP_LIVE filter(INDICATOR == "OILPROD",
%in% c("USA", "IRQ", "IRN")) %>%
LOCATION left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
left_join(DP_LIVE_var$INDICATOR, by = "INDICATOR") %>%
filter(!is.na(obsValue)) %>%
year_to_date() %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue/10^6/0.14) %>%
# 0.14 =
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
theme_minimal() + xlab("") + ylab("Billions of Barrels of Oil") +
+ scale_color_identity() +
add_3flags scale_y_log10(breaks = c(0.1, 0.2 ,0.5, 0.8, 1, 2, 3, 4, 5, 8, 10, 15, 20, 30),
labels = dollar_format(a = .1, pre = "", su = " Bn")) +
scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
labels = date_format("%Y"))
GGEXP - General government spending
Table
Code
%>%
DP_LIVE filter(INDICATOR == "GGEXP",
== "TOT",
SUBJECT == "PC_GDP") %>%
MEASURE left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION, Location) %>%
arrange(obsTime) %>%
summarise(date_min = min(obsTime),
date_max = max(obsTime),
obsValue = last(obsValue)) %>%
arrange(-obsValue) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
United States, Europe
Code
%>%
DP_LIVE filter(INDICATOR == "GGEXP",
%in% c("FRA", "USA"),
LOCATION == "TOT",
SUBJECT == "PC_GDP") %>%
MEASURE left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
year_to_date() %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue/100) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
theme_minimal() + xlab("") + ylab("General government spending") +
+ scale_color_identity() +
add_2flags scale_y_continuous(breaks = 0.01*seq(0, 260, 2),
labels = percent_format(accuracy = 1, p = "")) +
scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
labels = date_format("%Y"))
Spain, Italy, France, Germany, Portugal
Code
%>%
DP_LIVE filter(INDICATOR == "GGEXP",
%in% c("ESP", "ITA", "FRA", "DEU", "PRT", "USA"),
LOCATION == "TOT",
SUBJECT == "PC_GDP") %>%
MEASURE left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
year_to_date() %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue/100) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
theme_minimal() + xlab("") + ylab("General government spending (% of GDP)") +
+ scale_color_identity() +
add_6flags scale_y_continuous(breaks = 0.01*seq(0, 260, 2),
labels = percent_format(accuracy = 1, p = "")) +
scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
labels = date_format("%Y"))
France, Finland, Belgium, Norway, Denmark
Code
%>%
DP_LIVE filter(INDICATOR == "GGEXP",
%in% c("FRA", "FIN", "BEL", "NOR", "DNK"),
LOCATION == "TOT",
SUBJECT == "PC_GDP") %>%
MEASURE left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
year_to_date() %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue/100) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
theme_minimal() + xlab("") + ylab("General government spending") +
+ scale_color_identity() +
add_5flags scale_y_continuous(breaks = 0.01*seq(0, 260, 2),
labels = percent_format(accuracy = 1, p = "")) +
scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
labels = date_format("%Y"))
Sweden, Italy, Austria, Luxembourg, Greece
Code
%>%
DP_LIVE filter(INDICATOR == "GGEXP",
%in% c("SWE", "ITA", "AUT", "LUX", "GRC"),
LOCATION == "TOT",
SUBJECT == "PC_GDP") %>%
MEASURE left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
year_to_date() %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue/100) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
theme_minimal() + xlab("") + ylab("General government spending") +
+ scale_color_identity() +
add_5flags scale_y_continuous(breaks = 0.01*seq(0, 260, 2),
labels = percent_format(accuracy = 1, p = "")) +
scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
labels = date_format("%Y"))
Hungary, Germany, Australia, Iceland, Slovenia
Code
%>%
DP_LIVE filter(INDICATOR == "GGEXP",
%in% c("HUN", "DEU", "AUS", "ISL", "SVN"),
LOCATION == "TOT",
SUBJECT == "PC_GDP") %>%
MEASURE left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
year_to_date() %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue/100) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
theme_minimal() + xlab("") + ylab("General government spending") +
+ scale_color_identity() +
add_5flags scale_y_continuous(breaks = 0.01*seq(0, 260, 2),
labels = percent_format(accuracy = 1, p = "")) +
scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
labels = date_format("%Y"))
Slovak Republic, Portugal, Spain, Netherlands, Poland
Code
%>%
DP_LIVE filter(INDICATOR == "GGEXP",
%in% c("SVK", "PRT", "ESP", "NLD", "POL"),
LOCATION == "TOT",
SUBJECT == "PC_GDP") %>%
MEASURE left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
year_to_date() %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue/100) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
theme_minimal() + xlab("") + ylab("General government spending") +
+ scale_color_identity() +
add_5flags scale_y_continuous(breaks = 0.01*seq(0, 260, 2),
labels = percent_format(accuracy = 1, p = "")) +
scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
labels = date_format("%Y"))
Czech Republic, United Kingdom, Israel, Estonia, Japan
Code
%>%
DP_LIVE filter(INDICATOR == "GGEXP",
%in% c("CZE", "GBR", "ISR", "EST", "JPN"),
LOCATION == "TOT",
SUBJECT == "PC_GDP") %>%
MEASURE left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
year_to_date() %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue/100) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
theme_minimal() + xlab("") + ylab("General government spending") +
+ scale_color_identity() +
add_5flags scale_y_continuous(breaks = 0.01*seq(0, 260, 2),
labels = percent_format(accuracy = 1, p = "")) +
scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
labels = date_format("%Y"))
Latvia, United States, Lithuania, Switzerland, Costa Rica
Code
%>%
DP_LIVE filter(INDICATOR == "GGEXP",
%in% c("LVA", "USA", "LTU", "CHE", "CRI"),
LOCATION == "TOT",
SUBJECT == "PC_GDP") %>%
MEASURE left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
year_to_date() %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue/100) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
theme_minimal() + xlab("") + ylab("General government spending") +
+ scale_color_identity() +
add_5flags scale_y_continuous(breaks = 0.01*seq(0, 260, 2),
labels = percent_format(accuracy = 1, p = "")) +
scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
labels = date_format("%Y"))
Colombia, Korea, Chile, Ireland
Code
%>%
DP_LIVE filter(INDICATOR == "GGEXP",
%in% c("COL", "KOR", "CHL", "IRL"),
LOCATION == "TOT",
SUBJECT == "PC_GDP") %>%
MEASURE left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
year_to_date() %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue/100) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
theme_minimal() + xlab("") + ylab("General government spending") +
+ scale_color_identity() +
add_4flags scale_y_continuous(breaks = 0.01*seq(0, 260, 2),
labels = percent_format(accuracy = 1, p = "")) +
scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
labels = date_format("%Y"))
PASSCAR - Passenger car registrations
Table
Code
%>%
DP_LIVE filter(INDICATOR == "PASSCAR") %>%
left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION, Location, FREQUENCY) %>%
arrange(obsTime) %>%
summarise(date_min = min(obsTime),
date_max = max(obsTime)) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
HHDEBT - Household Debt
Table
Code
%>%
DP_LIVE filter(INDICATOR == "HHDEBT") %>%
left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION, Location) %>%
arrange(obsTime) %>%
summarise(date_min = min(obsTime),
date_max = max(obsTime)) %>%
arrange(as.numeric(date_min), -as.numeric(date_max)) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
France, Germany, Japan
Code
%>%
DP_LIVE filter(LOCATION %in% c("FRA", "DEU", "JPN"),
== "HHDEBT") %>%
INDICATOR %>%
year_to_date left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue / 100) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
theme_minimal() + xlab("") + ylab("Household Debt (% of net disposable income)") +
scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 200, 5),
labels = percent_format(accuracy = 1, prefix = "")) +
theme(legend.position = c(0.2, 0.90),
legend.title = element_blank())
Canada, Denmark, Finland
Code
%>%
DP_LIVE filter(LOCATION %in% c("CAN", "DNK", "FIN"),
== "HHDEBT") %>%
INDICATOR %>%
year_to_date left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue / 100) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
theme_minimal() + xlab("") + ylab("Household Debt (% of net disposable income)") +
scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 400, 10),
labels = percent_format(accuracy = 1, prefix = "")) +
theme(legend.position = c(0.2, 0.90),
legend.title = element_blank())
Japan, United Kingdom, United States
Code
%>%
DP_LIVE filter(LOCATION %in% c("JPN", "GBR", "USA"),
== "HHDEBT") %>%
INDICATOR %>%
year_to_date left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue / 100) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
theme_minimal() + xlab("") + ylab("Household Debt (% of net disposable income)") +
scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 400, 10),
labels = percent_format(accuracy = 1, prefix = "")) +
theme(legend.position = c(0.2, 0.90),
legend.title = element_blank())
GDPHRWKD - GDP Per hour worked
- USD (constant prices 2010 and PPPs)
Table
Code
%>%
DP_LIVE filter(INDICATOR == "GDPHRWKD") %>%
left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION, Location, MEASURE) %>%
arrange(obsTime) %>%
summarise(date_min = min(obsTime),
date_max = max(obsTime)) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
5 countries, IDX2010
Code
%>%
DP_LIVE filter(INDICATOR == "GDPHRWKD",
%in% c("GBR", "USA", "FRA", "JPN", "ITA"),
LOCATION == "IDX2010") %>%
MEASURE left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
year_to_date() %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + theme_minimal() + xlab("") + ylab("Average Wages") +
geom_line(aes(x = date, y = obsValue, color = color, linetype = Location)) +
#scale_linetype_manual(values = c("dotted", "solid", "longdash","solid", "solid")) +
+ scale_color_identity() +
add_5flags theme(legend.position = c(0.2, 0.85),
legend.title = element_blank()) +
scale_y_continuous(breaks = seq(0, 260, 5),
labels = scales::dollar_format(accuracy = 1, p = "", su = "")) +
scale_x_date(breaks = as.Date(paste0(seq(1700, 2020, 5), "-01-01")),
labels = date_format("%Y"))
U.S. Dollars
UK, US, France, Japan, Italy
Code
%>%
DP_LIVE filter(INDICATOR == "GDPHRWKD",
%in% c("GBR", "USA", "FRA", "JPN", "ITA"),
LOCATION == "USD") %>%
MEASURE left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
year_to_date() %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "FRA", color2, color)) %>%
ggplot(.) + theme_minimal() + xlab("") + ylab("Average Wages") +
geom_line(aes(x = date, y = obsValue, color = color, linetype = Location)) +
#scale_linetype_manual(values = c("dotted", "solid", "longdash","solid", "solid")) +
+ scale_color_identity() +
add_5flags theme(legend.position = c(0.2, 0.85),
legend.title = element_blank()) +
scale_y_continuous(breaks = seq(0, 260, 5),
labels = scales::dollar_format(accuracy = 1, p = "$", su = "")) +
scale_x_date(breaks = as.Date(paste0(seq(1700, 2020, 5), "-01-01")),
labels = date_format("%Y"))
Greece, UK
Code
%>%
DP_LIVE filter(INDICATOR == "GDPHRWKD",
%in% c("GRC", "GBR"),
LOCATION == "USD") %>%
MEASURE left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
year_to_date() %>%
filter(date >= as.Date("1990-01-01")) %>%
ggplot(.) + theme_minimal() + xlab("") + ylab("GDP Per Hour Worked (Source: OECD)") +
+
add_2flags geom_line(aes(x = date, y = obsValue, color = Location)) +
scale_color_manual(values = c("#0D5EAF", "#CF142B")) +
theme(legend.position = c(0.2, 0.85),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 260, 5),
labels = scales::dollar_format(accuracy = 1, p = "$")) +
scale_x_date(breaks = as.Date(paste0(seq(1700, 2020, 5), "-01-01")),
labels = date_format("%Y"))
Greece, UK, Italy
Code
%>%
DP_LIVE filter(INDICATOR == "GDPHRWKD",
%in% c("GRC", "GBR", "ITA"),
LOCATION == "USD") %>%
MEASURE left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
year_to_date() %>%
filter(date >= as.Date("1990-01-01")) %>%
ggplot(.) + theme_minimal() + xlab("") + ylab("GDP Per Hour Worked (Source: OECD)") +
+
add_3flags geom_line(aes(x = date, y = obsValue, color = Location)) +
scale_color_manual(values = c("#0D5EAF", "#009246", "#CF142B")) +
scale_linetype_manual(values = c("solid", "solid", "longdash","solid", "solid")) +
theme(legend.position = c(0.2, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 260, 5),
labels = scales::dollar_format(accuracy = 1, p = "$")) +
scale_x_date(breaks = as.Date(paste0(seq(1700, 2020, 5), "-01-01")),
labels = date_format("%Y"))
AVWAGE - General government debt
Table
Code
%>%
DP_LIVE filter(INDICATOR == "AVWAGE") %>%
left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION, Location) %>%
arrange(obsTime) %>%
summarise(date_min = min(obsTime),
date_max = max(obsTime)) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
5 countries
Code
%>%
DP_LIVE filter(INDICATOR == "AVWAGE",
%in% c("GBR", "USA", "FRA", "JPN", "ITA")) %>%
LOCATION left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
year_to_date() %>%
ggplot(.) + theme_minimal() + xlab("") + ylab("Average Wages") +
geom_line(aes(x = date, y = obsValue / 1000, color = Location, linetype = Location)) +
scale_color_manual(values = c("#000000", "#009246", "#BC002D", "#6E82B5", "#B22234")) +
scale_linetype_manual(values = c("solid", "solid", "longdash","solid", "solid")) +
geom_image(data = . %>%
filter(date == as.Date("1990-01-01")) %>%
mutate(date = as.Date("1990-01-01"),
image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Location)), ".png")),
aes(x = date, y = obsValue/1000, image = image), asp = 1.5) +
theme(legend.position = c(0.2, 0.85),
legend.title = element_blank()) +
scale_y_continuous(breaks = seq(0, 260, 5),
labels = scales::dollar_format(accuracy = 1, p = "$", su = "k")) +
scale_x_date(breaks = as.Date(paste0(seq(1700, 2020, 5), "-01-01")),
labels = date_format("%Y"))
GGDEBT - General government debt
Table
Code
%>%
DP_LIVE filter(INDICATOR == "GGDEBT") %>%
left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION, Location) %>%
arrange(obsTime) %>%
summarise(date_min = min(obsTime),
date_max = max(obsTime)) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
5 countries
Code
%>%
DP_LIVE filter(INDICATOR == "GGDEBT",
%in% c("GBR", "USA", "FRA", "JPN", "ITA")) %>%
LOCATION left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
year_to_date() %>%
ggplot(.) + theme_minimal() + xlab("") + ylab("Government Debt (% of GDP)") +
geom_line(aes(x = date, y = obsValue / 100, color = Location, linetype = Location)) +
scale_color_manual(values = c("#000000", "#009246", "#BC002D", "#6E82B5", "#B22234")) +
scale_linetype_manual(values = c("solid", "solid", "longdash","solid", "solid")) +
geom_image(data = . %>%
filter(date == as.Date("1997-01-01")) %>%
mutate(date = as.Date("1997-01-01"),
image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Location)), ".png")),
aes(x = date, y = obsValue/100, image = image), asp = 1.5) +
theme(legend.position = c(0.2, 0.85),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 260, 20),
labels = scales::percent_format(accuracy = 1)) +
scale_x_date(breaks = as.Date(paste0(seq(1700, 2020, 5), "-01-01")),
labels = date_format("%Y"))
5 countries
Code
%>%
DP_LIVE filter(INDICATOR == "GGDEBT",
%in% c("GBR", "USA", "DEU", "JPN", "ITA", "FRA")) %>%
LOCATION left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
year_to_date() %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue/100) %>%
ggplot(.) + theme_minimal() + xlab("") + ylab("Dette publique (années de PIB)") +
geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_6flags +
scale_linetype_manual(values = c("solid", "solid", "longdash","solid", "solid")) +
theme(legend.position = c(0.2, 0.85),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 260, 20),
labels = scales::dollar_format(acc = .1, pre = "", su = " années")) +
scale_x_date(breaks = as.Date(paste0(seq(1700, 2020, 5), "-01-01")),
labels = date_format("%Y"))
Labour force participation rate
Table, 2019
Code
%>%
DP_LIVE filter(obsTime == "2019",
== "LFPR") %>%
INDICATOR select(LOCATION, SUBJECT, obsValue) %>%
left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
mutate(obsValue = round(obsValue, 1)) %>%
spread(SUBJECT, obsValue) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
Netherlands, Germany, Japan
Code
%>%
DP_LIVE filter(LOCATION %in% c("DEU", "JPN", "NLD"),
== "LFPR",
INDICATOR == "15_64") %>%
SUBJECT left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
year_to_date() %>%
ggplot() + geom_line(aes(x = date, y = obsValue/100, color = Location)) +
scale_color_manual(values = c("#000000", "#BC002D", "#21468B")) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.15, 0.8),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-6, 90, 2),
labels = percent_format(accuracy = 1)) +
ylab("Labour force participation rate") + xlab("")
Employment Rates
Netherlands, Germany, Japan
Code
%>%
DP_LIVE filter(LOCATION %in% c("DEU", "JPN", "NLD"),
== "EMP",
INDICATOR == "TOT",
SUBJECT == "PC_WKGPOP") %>%
MEASURE left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
year_to_date() %>%
mutate(obsValue = obsValue/100) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = Location)) +
scale_color_manual(values = c("#000000", "#BC002D", "#21468B")) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.15, 0.8),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-6, 90, 2),
labels = percent_format(accuracy = 1)) +
ylab("Employment rates") + xlab("")
France, Italy, Spain
Code
%>%
DP_LIVE filter(LOCATION %in% c("FRA", "ITA", "ESP"),
== "EMP",
INDICATOR == "TOT",
SUBJECT == "PC_WKGPOP") %>%
MEASURE left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
year_to_date() %>%
mutate(obsValue = obsValue/100) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = Location)) +
scale_color_manual(values = c("#002395", "#009246", "#C60B1E")) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.15, 0.8),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-6, 90, 2),
labels = percent_format(accuracy = 1)) +
ylab("Part-time employment rate") + xlab("")
Gross Pension Replacement Rates
Men
Code
%>%
DP_LIVE filter(LOCATION %in% c("DEU", "FRA", "SWE"),
== "GPENSION",
INDICATOR == "MEN") %>%
SUBJECT left_join(DP_LIVE_var$LOCATION %>%
setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
year_to_date() %>%
mutate(obsValue = obsValue/100) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = Location)) +
scale_color_manual(values = c("#ED2939", "#000000", "#006AA7")) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.35, 0.3),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-6, 90, 2),
labels = percent_format(accuracy = 1)) +
ylab("Gross Pension Replacement Rate") + xlab("")
Women
Code
%>%
DP_LIVE filter(LOCATION %in% c("DEU", "FRA", "SWE"),
== "GPENSION",
INDICATOR == "WOMEN") %>%
SUBJECT left_join(DP_LIVE_var$LOCATION %>%
setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
year_to_date() %>%
mutate(obsValue = obsValue/100) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = Location)) +
scale_color_manual(values = c("#ED2939", "#000000", "#006AA7")) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.35, 0.3),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-6, 90, 2),
labels = percent_format(accuracy = 1)) +
ylab("Gross Pension Replacement Rate") + xlab("")
INPROD
Just Manufacturing
Japan, United Kingdom, USA
Code
%>%
DP_LIVE filter(LOCATION %in% c("JPN", "GBR", "USA"),
== "INDPROD",
INDICATOR == "MFG",
SUBJECT == "M") %>%
FREQUENCY %>%
month_to_date left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
theme_minimal() + xlab("") + ylab("Industrial Production") +
scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(-10, 400, 10))
Spain, Italy, France, Germany
All
Code
%>%
DP_LIVE filter(LOCATION %in% c("ESP", "ITA", "FRA", "DEU"),
== "INDPROD",
INDICATOR == "MFG",
SUBJECT == "M") %>%
FREQUENCY %>%
month_to_date left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_4flags +
theme_minimal() + xlab("") + ylab("Industrial Production") +
scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(-10, 400, 10))
1990-
Code
%>%
DP_LIVE filter(LOCATION %in% c("ESP", "ITA", "FRA", "DEU"),
== "INDPROD",
INDICATOR == "MFG",
SUBJECT == "M") %>%
FREQUENCY %>%
month_to_date filter(date >= as.Date("1990-01-01")) %>%
left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION) %>%
arrange(date) %>%
mutate(obsValue = 100*obsValue/obsValue[1]) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_4flags +
theme_minimal() + xlab("") + ylab("Industrial Production") +
scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(-10, 400, 10))
Germany
All
Code
%>%
DP_LIVE filter(INDICATOR == "INDPROD",
== "DEU",
LOCATION == "M") %>%
FREQUENCY left_join(DP_LIVE_var$SUBJECT, by = "SUBJECT") %>%
%>%
month_to_date + geom_line(aes(x = date, y = obsValue, color = Subject))+
ggplot theme_minimal() + xlab("") + ylab("Industrial Production, Index") +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(-10, 200, 10),
labels = dollar_format(accuracy = 1, prefix = "")) +
scale_color_manual(values = viridis(5)[1:4]) +
theme(legend.position = c(0.8, 0.20),
legend.title = element_blank())
1990-
Code
%>%
DP_LIVE filter(INDICATOR == "INDPROD",
== "DEU",
LOCATION == "M") %>%
FREQUENCY left_join(DP_LIVE_var$SUBJECT, by = "SUBJECT") %>%
%>%
month_to_date filter(date >= as.Date("1990-01-01")) %>%
+ geom_line(aes(x = date, y = obsValue, color = Subject))+
ggplot theme_minimal() + xlab("") + ylab("Industrial Production, Index") +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(-10, 200, 10),
labels = dollar_format(accuracy = 1, prefix = "")) +
scale_color_manual(values = viridis(4)[1:3]) +
theme(legend.position = c(0.8, 0.20),
legend.title = element_blank())
France
All
Code
%>%
DP_LIVE filter(INDICATOR == "INDPROD",
== "FRA",
LOCATION == "M") %>%
FREQUENCY left_join(DP_LIVE_var$SUBJECT %>%
setNames(c("SUBJECT", "Subject")), by = "SUBJECT") %>%
%>%
month_to_date + geom_line(aes(x = date, y = obsValue, color = Subject))+
ggplot theme_minimal() + xlab("") + ylab("Industrial Production, Index") +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(-10, 200, 10),
labels = dollar_format(accuracy = 1, prefix = "")) +
scale_color_manual(values = viridis(4)[1:3]) +
theme(legend.position = c(0.8, 0.20),
legend.title = element_blank())
1990-
Code
%>%
DP_LIVE filter(INDICATOR == "INDPROD",
== "FRA",
LOCATION == "M") %>%
FREQUENCY left_join(DP_LIVE_var$SUBJECT %>%
setNames(c("SUBJECT", "Subject")), by = "SUBJECT") %>%
%>%
month_to_date filter(date >= as.Date("1990-01-01")) %>%
+ geom_line(aes(x = date, y = obsValue, color = Subject))+
ggplot theme_minimal() + xlab("") + ylab("Industrial Production, Index") +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(-10, 200, 10),
labels = dollar_format(accuracy = 1, prefix = "")) +
scale_color_manual(values = viridis(4)[1:3]) +
theme(legend.position = c(0.8, 0.20),
legend.title = element_blank())
U.S.
Code
%>%
DP_LIVE filter(INDICATOR == "INDPROD",
== "USA",
LOCATION == "M") %>%
FREQUENCY left_join(DP_LIVE_var$SUBJECT %>%
setNames(c("SUBJECT", "Subject")), by = "SUBJECT") %>%
%>%
month_to_date + geom_line(aes(x = date, y = obsValue, color = Subject))+
ggplot theme_minimal() + xlab("") + ylab("Industrial Production, Index") +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(-10, 200, 10),
labels = dollar_format(accuracy = 1, prefix = "")) +
scale_color_manual(values = viridis(5)[1:4]) +
theme(legend.position = c(0.8, 0.20),
legend.title = element_blank())
LT interest rates
All
Code
%>%
DP_LIVE filter(INDICATOR == "LTINT",
%in% c("USA", "FRA", "DEU"),
LOCATION == "M") %>%
FREQUENCY left_join(DP_LIVE_var$LOCATION %>%
setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
%>%
month_to_date + geom_line(aes(x = date, y = obsValue/100, color = Location)) +
ggplot geom_image(data = . %>%
filter(date == as.Date("1982-01-01")) %>%
mutate(date = as.Date("1982-01-01"),
image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Location)), ".png")),
aes(x = date, y = obsValue/100, image = image), asp = 1.5) +
theme_minimal() + xlab("") + ylab("Long-term interest rates") +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-2, 30, 1),
labels = percent_format(accuracy = 1)) +
scale_color_manual(values = c("#002395", "#000000", "#B22234")) +
theme(legend.position = c(0.8, 0.80),
legend.title = element_blank())
2000-
Code
%>%
DP_LIVE filter(INDICATOR == "LTINT",
%in% c("USA", "FRA", "DEU"),
LOCATION == "M") %>%
FREQUENCY left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
%>%
month_to_date filter(date >= as.Date("2000-01-01")) %>%
+ geom_line(aes(x = date, y = obsValue/100, color = Location))+
ggplot geom_image(data = . %>%
filter(date == as.Date("2018-07-01")) %>%
mutate(date = as.Date("2018-07-01"),
image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Location)), ".png")),
aes(x = date, y = obsValue/100, image = image), asp = 1.5) +
theme_minimal() + xlab("") + ylab("Long-term interest rates") +
scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-2, 30, 1),
labels = percent_format(accuracy = 1)) +
scale_color_manual(values = c("#002395", "#000000", "#B22234")) +
theme(legend.position = c(0.8, 0.80),
legend.title = element_blank())
Part Time Employment Rate
Table
Code
%>%
DP_LIVE filter(INDICATOR == "PARTEMP",
== "TOT") %>%
SUBJECT group_by(LOCATION) %>%
summarise(Nobs = n())
# # A tibble: 52 × 2
# LOCATION Nobs
# <chr> <int>
# 1 AUS 19
# 2 AUT 28
# 3 BEL 40
# 4 BGR 23
# 5 BRA 21
# 6 CAN 47
# 7 CHE 27
# 8 CHL 27
# 9 COL 21
# 10 CRI 13
# # ℹ 42 more rows
Netherlands, Germany, Japan
Code
%>%
DP_LIVE filter(LOCATION %in% c("DEU", "JPN", "NLD"),
== "PARTEMP",
INDICATOR == "TOT") %>%
SUBJECT left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
year_to_date() %>%
mutate(obsValue = obsValue/100) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = Location)) +
scale_color_manual(values = c("#000000", "#BC002D", "#21468B")) + add_3flags +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.15, 0.8),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-6, 90, 2),
labels = percent_format(accuracy = 1)) +
ylab("Part-time employment rate") + xlab("")
Netherlands, France, Germany, Japan
Code
%>%
DP_LIVE filter(LOCATION %in% c("DEU", "FRA", "JPN", "NLD"),
== "PARTEMP",
INDICATOR == "TOT") %>%
SUBJECT left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
year_to_date() %>%
mutate(obsValue = obsValue/100) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = Location)) +
+
add_4flags scale_color_manual(values = c("#002395", "#000000", "#BC002D", "#21468B")) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.15, 0.8),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-6, 90, 2),
labels = percent_format(accuracy = 1)) +
ylab("Part-time employment rate") + xlab("")
Germany, France
English
All
Code
%>%
DP_LIVE filter(LOCATION %in% c("DEU", "FRA"),
== "PARTEMP",
INDICATOR == "TOT") %>%
SUBJECT left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
year_to_date() %>%
mutate(obsValue = obsValue/100) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = Location)) +
scale_color_manual(values = c("#002395", "#000000")) +
geom_image(data = . %>%
filter(date == as.Date("2006-01-01")) %>%
mutate(image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Location)), ".png")),
aes(x = date, y = obsValue, image = image), asp = 1.5) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.15, 0.8),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-6, 90, 2),
labels = percent_format(accuracy = 1)) +
ylab("Part-time employment rate") + xlab("")
1995-
Code
%>%
DP_LIVE filter(LOCATION %in% c("DEU", "FRA"),
== "PARTEMP",
INDICATOR == "TOT") %>%
SUBJECT left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
year_to_date() %>%
mutate(obsValue = obsValue/100) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = Location)) +
scale_color_manual(values = c("#002395", "#000000")) +
geom_image(data = . %>%
filter(date == as.Date("2006-01-01")) %>%
mutate(image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Location)), ".png")),
aes(x = date, y = obsValue, image = image), asp = 1.5) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.15, 0.8),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-6, 90, 2),
labels = percent_format(accuracy = 1)) +
ylab("Taux d'emploi à temps partiel (%)") + xlab("")
French
Code
%>%
DP_LIVE filter(LOCATION %in% c("DEU", "FRA"),
== "PARTEMP",
INDICATOR == "TOT") %>%
SUBJECT left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
year_to_date() %>%
mutate(obsValue = obsValue/100) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = Location)) +
scale_color_manual(values = c("#002395", "#000000")) +
geom_image(data = . %>%
filter(date == as.Date("2006-01-01")) %>%
mutate(image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Location)), ".png")),
aes(x = date, y = obsValue, image = image), asp = 1.5) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-6, 90, 2),
labels = percent_format(accuracy = 1),
limits = c(0, 0.24)) +
ylab("Taux d'emploi à temps partiel") + xlab("")
French
Code
%>%
DP_LIVE filter(LOCATION %in% c("DEU", "FRA"),
== "PARTEMP",
INDICATOR == "TOT") %>%
SUBJECT left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
year_to_date() %>%
mutate(obsValue = obsValue/100) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = Location)) +
scale_color_manual(values = c("#002395", "#000000")) +
geom_image(data = . %>%
filter(date == as.Date("2006-01-01")) %>%
mutate(image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Location)), ".png")),
aes(x = date, y = obsValue, image = image), asp = 1.5) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-6, 90, 2),
labels = percent_format(accuracy = 1),
limits = c(0, 0.24)) +
ylab("Taux d'emploi à temps partiel") + xlab("")
Tourism GDP
Code
%>%
DP_LIVE filter(INDICATOR == "TOUR_GDP") %>%
left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
arrange(LOCATION, obsTime) %>%
group_by(LOCATION, Location) %>%
summarise(year = last(obsTime),
`Tourism (% of GDP)` = round(last(obsValue), 1)) %>%
arrange(-`Tourism (% of GDP)`) %>%
mutate(`Tourism (% of GDP)` = paste0(`Tourism (% of GDP)`, "%")) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
Social Spending - SOCEXP, PENSIONEXP
Table
Code